Male vs. Female Blood Sugar Levels (HbA1c)

>>>>>>> 3269be02134749ea203553e47da303a47edbc66e

Similar Prevalence of Prediabetes – The proportion of individuals categorized as having prediabetes (HbA1c 5.7% - 6.4%) is almost identical between males (41.3%) and females (41.4%). This suggests that prediabetes affects both genders at nearly the same rate.

• Slightly Higher Diabetes Rates Among Males – More males (21.6%) fall into the diabetes (HbA1c ≥ 6.5%) category compared to females (20.2%). While the difference is small, it might indicate that men have a slightly higher risk of diabetes in this dataset.

Females Have a Slightly Higher Proportion of Normal Blood Sugar Levels – More females (38.4%) fall into the normal blood sugar category (<5.7%) compared to males (37.1%). This may indicate some slight protective factors or lifestyle differences in this group.

Since more males are in the diabetes category, there could be gender-related risk factors worth exploring—such as diet, activity levels, or genetic predisposition.

Overall, blood sugar regulation patterns appear fairly balanced between genders, but small differences suggest potential areas for further investigation.

Similar Prevalence of Prediabetes The proportion of individuals classified as having prediabetes (HbA1c 5.7% - 6.4%) is nearly identical between males (41.3%) and females (41.4%). This suggests no significant disparity.

99,982 x 4 (first 5 rows)

gender	diabetes	HbA1c_level	HbA1c_category
Female	0	6.6	Diabetic ≥ 6.5%
Female	0	6.6	Diabetic ≥ 6.5%
Male	0	5.7	Prediabetic 5.7% - 6.4%
Female	0	5.0	Normal < 5.7%
Male	0	4.8	Normal < 5.7%

6 x 4 (first 5 rows)

gender	HbA1c_category	n	percent
Female	Diabetic ≥ 6.5%	11835	20.21280
Female	Normal < 5.7%	22492	38.41372
Female	Prediabetic 5.7% - 6.4%	24225	41.37348
Male	Diabetic ≥ 6.5%	8959	21.62443
Male	Normal < 5.7%	15358	37.06976

<<<<<<< HEAD

Age Distribution in Diabetes, Heart Disease, and Hypertension

>>>>>>> 3269be02134749ea203553e47da303a47edbc66e

358 x 5 (first 5 rows)

age	diabetes	heart_disease	hypertension	group
57	1	1	1	Diabetes, H.D, and Hyp.
62	1	1	1	Diabetes, H.D, and Hyp.
62	1	1	1	Diabetes, H.D, and Hyp.
67	1	1	1	Diabetes, H.D, and Hyp.
72	1	1	1	Diabetes, H.D, and Hyp.

81,885 x 5 (first 5 rows)

age	heart_disease	diabetes	hypertension	group
54	0	0	0	Free of Diabetes, H.D, and Hyp.
28	0	0	0	Free of Diabetes, H.D, and Hyp.
36	0	0	0	Free of Diabetes, H.D, and Hyp.
20	0	0	0	Free of Diabetes, H.D, and Hyp.
79	0	0	0	Free of Diabetes, H.D, and Hyp.

<<<<<<< HEAD

BMI Distribution by Hypertension Status Plot

>>>>>>> 3269be02134749ea203553e47da303a47edbc66e

Shows the distribution of BMI values based on hypertension status. A violin plot is great for visualizing the distribution and density of BMI across hypertension categories,

Shape and width: The width of each “violin” represents the density of BMI values at different levels. Wider sections mean more individuals have that BMI, while narrower sections indicate fewer people at those values.

Comparison of distributions: The blue violin represents people without hypertension (hypertension = 0), while the red violin represents those with hypertension (hypertension = 1). By comparing them, you can see how BMI differs between these groups.

The horizontal line around 25 BMI: This marks the median BMI for each group. Since both violins have a horizontal line in roughly the same position, it suggests that the median BMI is around 25 for both hypertensive and non-hypertensive individuals.

Density trends: If the violins have different thicknesses in certain BMI ranges, it tells you which BMI values are more or less common in each group. People with hypertension seem to have a higher BMI overall, but both groups share a similar median.

The distribution shape is different—for example, if one violin is wider at higher BMI values, it suggests that hypertension is more common among individuals with higher BMI.

Outliers or extreme values might appear as small bulges or extended tails at the ends of the violins, showing individuals with very high or low BMI.

10,000 x 9 (first 5 rows)

gender	age	hypertension	heart_disease	smoking_history	bmi	HbA1c_level	blood_glucose_level	diabetes
Female	80	0	1	never	25.19	6.6	140	0
Female	54	0	0	No Info	27.32	6.6	80	0
Male	28	0	0	never	27.32	5.7	158	0
Female	36	0	0	current	23.45	5.0	155	0
Male	76	1	1	current	20.14	4.8	155	0

<<<<<<< HEAD ======= >>>>>>> 3269be02134749ea203553e47da303a47edbc66e

The graph below is separated by whether or not a person has hypertension. With the comparison of BMI as the range, it’s seen that majority of people with and without hypertension lie within a BMI range of 25-29. Notice that for people with hypertension, the desnity population above the red line is greater than that of people without hypertension; indicating that there’s a larger of population of people with hypertension that have a larger BMI

<<<<<<< HEAD

BMI vs. Age Across Diabetes & Heart Disease Plot

A relation to BMI and Heart Disease

>>>>>>> 3269be02134749ea203553e47da303a47edbc66e

Each person within this scale has heart disease. Here a comparison is made between declared underweight and overweight people, grouped by sex, based on a BMI scale. There’s a significant increase in population percentage for those who are considered overweight and that have heart disease. With visual aid, it can be concluded that as weight increases, chances of heart disease will increase.

<<<<<<< HEAD

# Figure out the unique categories of smoking history
unique(diabetes_dataset$smoking_history)

## [1] "never"       "No Info"     "current"     "former"      "ever"       
## [6] "not current"

# Count amount of people who belong to each unique smoking category
#Omit No info

smoking_tally <- diabetes_dataset %>% filter(smoking_history != 'No Info') %>%  group_by(smoking_history) %>% summarise(total_people = n())

#group diabetic vs non diabetic people together

smoking_diabetes_dataset <- diabetes_dataset %>%
  filter(smoking_history != 'No Info') %>%  
  group_by(smoking_history, diabetes) %>%  
  summarise(total = n())

## `summarise()` has grouped output by 'smoking_history'. You can override using
## the `.groups` argument.

smoking_diabetes_dataset

## # A tibble: 10 × 3
## # Groups:   smoking_history [5]
##    smoking_history diabetes total
##    <chr>              <dbl> <int>
##  1 current                0  8338
##  2 current                1   948
##  3 ever                   0  3532
##  4 ever                   1   472
##  5 former                 0  7762
##  6 former                 1  1590
##  7 never                  0 31749
##  8 never                  1  3346
##  9 not current            0  5757
## 10 not current            1   690

# Inner join tally data with diabetic grouped data;
#mutate a column to create a percentage per category;
#select desired columns

smoking_diabetes_percentage <- inner_join(smoking_tally, smoking_diabetes_dataset, by = 'smoking_history') %>% mutate(Percentage = total/total_people *100) %>% select(smoking_history, diabetes, total, Percentage)

smoking_diabetes_percentage

## # A tibble: 10 × 4
##    smoking_history diabetes total Percentage
##    <chr>              <dbl> <int>      <dbl>
##  1 current                0  8338      89.8 
##  2 current                1   948      10.2 
##  3 ever                   0  3532      88.2 
##  4 ever                   1   472      11.8 
##  5 former                 0  7762      83.0 
##  6 former                 1  1590      17.0 
##  7 never                  0 31749      90.5 
##  8 never                  1  3346       9.53
##  9 not current            0  5757      89.3 
## 10 not current            1   690      10.7

library(ggplot2)
library(plotly)

#Create initial graph about smoking and diabetes

smoking_diabetes_graph <- ggplot(smoking_diabetes_percentage) + 
  geom_col(aes(x = smoking_history, y = total, fill = as.factor(diabetes)), position = 'dodge') 

smoking_diabetes_graph <- smoking_diabetes_graph + coord_flip() + labs(
  title = 'Smoking History and Diabetes Relationship',
  y = 'People',
  x = 'Smoking History',
  fill = 'Has Diabetes',
  caption = "1 indicates diabetes, 0 indicates no diabetes"
  ) 

smoking_diabetes_graph

ggplot(smoking_diabetes_percentage) + geom_col(aes(x = smoking_history, y = Percentage, fill = as.factor(diabetes)))